Major problems in delivery of safe and effective care services in hospitals involve deficiencies in the quality and continuity of patient care, including the monitoring of each patient's condition over time. Despite recent advances in electronic health records (EHR) systems, the present state of the art in medical care within hospitals still does not in general utilize the accruing medical record information for active, prognostic use-cases, to predict the future status or events or outcomes that are likely to materialize for the patient. Instead, in many scenarios the EHR acts mainly as a passive repository for documenting and storing the information that is generated by each provider and each department, which characterizes the current or previous status or outcomes that have already materialized.
During a typical hospital stay, each patient may see many doctors and many nurses. Such fragmentation of responsibility for the care process challenges the ability of each provider to quickly and accurately grasp the meaning of the constellation of accumulating clinical and laboratory facts about the patient, to understand trends that may be developing in the patient's health status, and to evaluate the urgency of attention that is necessary to effectively address existing or newly developing issues or to successfully prevent potential adverse events and complications.
The consequence of the proliferation of medical information in each patient acute care episode, combined with the all-too-common fragmentation of the care process with responsibilities divided among dozens of provider personnel most of whom do not have deep or longstanding familiarity with the patient, is that unexpected physiologic deterioration occurs to many patients, especially post-operatively or post-medical procedures, such that a medical crisis ensues. Precious care resources of the hospital are diverted in an attempt to save the patient, and needless suffering and even death occur. In many such instances, the impending deterioration could have been predicted—provided that enough vital signs and other monitoring data were acquired in advance; provided that that data were integrated into a suitably accurate personalized predictive model; and provided that the output of the model were effectively communicated to the providers who have the responsibility to intervene and prevent or manage the predicted risk of acute deterioration.
While the recognition and interpretation of some acute events such as loss of consciousness or dyspnea or new onset of fever or decompensation of blood pressure or other hemodynamic parameters are clear-cut, in other cases the events or changes are not readily recognized or interpreted, particularly by personnel who have not previously been involved in the patient's care. A change in systolic blood pressure (SBP) to 180 mm Hg might for one person be of ominous and critical significance (for example, in a person whose usual SBP is 110 mm Hg), but carries no adverse prognostic significance for a person whose chronic, poorly controlled hypertension is associated with a usual SBP of 190 mm Hg.
Life-threatening deterioration of patients' health status while in a hospital is often preceded by abnormalities in hemodynamic variables and organ-system parameters measured by clinical and laboratory tests. Over the past several years there have emerged a variety of rapid response team (RRT) and early-warning system (EWS, MEWS, PARS, etc.) methods that aim to combine such information and calculate an index or score that can be used to gauge the risk of acute deterioration and, if the risk is sufficiently high, notify the responsible physicians, transfer the patient to an alternate location where intensified monitoring and care services can be provided, and/or undertake other actions to prevent or mitigate the predicted deterioration.
Frequently, however, there is no obvious or apparent abnormality in vital signs or other clinical or laboratory variables that precedes the deterioration and, in such instances, the RRT- and MEWS-type calculations fail, giving a ‘false-negative’ assurance that there will be no near-term deterioration in the patient's status when in fact deterioration does materialize. A Hodgetts Score=7 has only a sensitivity of 64%, and Score=8 yields sensitivity of only 52%, for example. In other words, in 36% and 48% of cases, respectively, a false-negative interpretation is ascribed and the Hodgetts score fails to alert the caregivers to the deterioration that ensues.
In other instances, fluctuations in the values of physiologic variables that are utilized by an RRT or MEWS-type calculations give rise to ‘false-positive’ alarms, incorrectly identifying a given patient as one in whom acute deterioration is likely when in fact no deterioration occurs. In such a situation, valuable resources associated with intensified monitoring or other interventions are misapplied. The resources are allocated to the given patient, in whom those resources are not in fact necessary and provide no benefit, and, insofar as resources are finite and in short supply, those resources are during that same time interval withheld from other patients, for whom the resources might have provided greater value and benefit.
Thus, a significant limitation of a number of existing models for determining or predicting patient deterioration in health is of limited statistical sensitivity and specificity, with substantial false-negative and false-positive rates. Most of the commonly applied regression equations or CART or decision-tree or neural-network or other classification algorithms are able only to achieve receiver operating characteristic (ROC) area-under-the-curve (AUC) discrimination performance of approximately 75% to 80%. Certain existing models achieve ROC AUC of up to 90% in selected subpopulations, such as patients in an emergency department.
Another significant limitation looking at the current state of the art is that the variables that are included in the predictions are often temporally ‘lagging indicators’ (such as serum creatinine or other metabolic indicators of kidney function), which broadly characterize a background of diminished organ-system capacity or organ-system vulnerability to physiologic stressors. But it is a background that is at the time of calculation of the RRT- or MEWS-type score already obvious to the physicians who are managing the patient's care. The RRT- or MEWS-type score does not tell the physicians anything that they do not already know. The same is also true for variables that are not temporally ‘lagging’ ones.
For example, ‘threatened airway’ (or ‘respiratory rate <5 bpm or >30 bpm’) is included in several models of the prior art, but this is not a variable that should require elaborate calculations to interpret, nor should decision-making regarding whether to intervene or intensify monitoring of the patient await computation of a multivariable score that incorporates such variables.
An acute change in mental status (such as is often measured by Glasgow Coma Score or other scales) is likewise intuitively obvious with regard to portending increased risk of further deterioration or adverse events. An acute change in body temperature is another example of a self-evident or ‘obvious’ indication of acutely altered risk of acute deterioration. The risk that is entailed by such information is obvious on its face, and an index or score that references these variables adds little value toward prediction or anticipatory decision-making to prevent declining health or to manage adverse events that have not thus far materialized. Such ‘obvious’ information and scores derived from them primarily serve (a) as a post-facto form of concise documentation of the materialized abnormalities and associated, already-obvious increased risk and (b) as a means of triaging or prioritizing patients according to already-materialized severity of illness.
Still further, another limitation of the existing art is that the predictive models typically rely upon measurements that are often performed in an imprecise and inconsistent manner. For example, measurement of diastolic blood pressure (DBP) by auscultation of Korotkoff sounds with a stethoscope and blood pressure cuff ought in principle to be a relatively accurate and precise process. However, haste and poor technique on the part of the observer often cause DBP measurements to be in error by many millimeters of mercury. It is difficult to compel improvements by busy caregivers who are prone to make imprecise and inconsistent measurements. As a result, any point-estimate or single-point-in-time predictor that is based on variables whose values tend to be subject to inaccuracy, imprecision and inconsistency in measurement technique tend to generate wide variations in predicted risk. By contrast, variables whose measurements do not present such difficulties (such as systolic blood pressure SBP and heart rate HR) are amenable to more accurate, precise predictions.